Finite-blocklength coding (FBC) is a promising technology to achieve ultra-reliable and low-latency communications (URLLC) in emerging applications, e.g., autonomous driving and extended reality. The challenge lies in scheduling random arriving traffic in URLLC due to the blocklength constraint imposed by low latency. This paper designs a cross-layer mechanism, called the joint scheduling and FBC policy, to meet URLLC requirements for bursty traffic under AWGN and block fading channels. First, we model a single-user transmission system as a tandem queue model. In this model, a packet queue buffers randomly arriving packets. After these packets are encoded using FBC, the resulting encoded symbols are buffered in a symbol queue. To analyze the latency and reliability performance, we represent the system as a Markov chain and conduct steady-state and transition analyses. After that, we construct a non-convex problem to minimize delay subject to reliability and power constraints. Using a variable combination method, we convert this problem into a linear-fractional programming (LFP) problem. Notice that extremely high reliable requirement significantly increases the computational complexity of standard LFP method.We approximate the objective function and packet drop ratio constraint, and obtain a lower bound of the minimum average delay effectively with a marginal performance loss.

This paper considers a real-time scheduling problem disseminating status update of sensors timely from a base station (BS) to users over wireless fading channels. The freshness of the updated status is quantified using the Age of Incorrect Information (AoII) metric. The objective is to minimize the long-term AoII under the constraint of limited transmission power, which necessitates that only a subset of users can successfully receive updates in each time slot. To find an optimal transmission policy, we first model the AoII minimization problem as a multi-action multi-armed bandit problem. After that, we decompose the derived MAB problem into multiple sub-problems. For each sub-problem, we obtain an optimal policy based on multiple threshold, thereby establishing the indexability of the optimal problem. Building upon this, a novel multi-action productivity index (MAPI) is introduced to get the optimal transmission policy. However, due to the computational complexity of the relative value iteration (RVI) algorithm, the exact value of the MAPI remains difficult to determine. To address this challenge, a computationally efficient algorithm is proposed to approximate the indices and derive the transmission policy for each user. Compared with Whittle’s Index and Max Weight policies, MAPI-based policy demonstrates significant performance improvement, particularly in large-scale networks and when the transmission power separation interval is small.

This paper investigates the lazy secret sharing problem from an information-theoretic perspective. The participants are classified into two categories: Lazy-Participants and Share-Participants. The objective is to guarantee the perfect secret recovery from any t participants, while ensuring security exclusively for Share-Participants. The optimal coding rate region for lazy secret sharing is characterized. We further consider the imperfect security formulation, formulating security through information leakage constraints rather than strict security. The optimal rate region in the imperfect formulation is also established for a specific symmetric scenario.

The increasing demand for Extended Reality (XR) services in 6G networks calls for advanced wireless transmission systems capable of supporting immersive experience, sustainability, and stringent security and privacy guarantees. This paper investigates an energy-efficient XR transmission system that ensures high user-perceived quality under hard-latency constraints, while simultaneously meeting reliability and security requirements in the Finite Blocklength (FBL) regime. Specifically, the XR system is modeled as a finite-horizon Markov Decision Process (MDP), where a power allocation scheme is employed to satisfy FBL reliability and security requirements. Leveraging this MDP formulation, we construct a transmission power minimization problem subject to a user-perceived quality constraint that can maintain a stable playback rate at the receiver within a strict latency deadline. The minimum transmission power is then characterized through Bellman optimality equations of the value function, and the optimal XR transmission policy is derived using a Dynamic Programming (DP) approach. In addition, the proposed framework is extended to accommodate practical system non-idealities, including random XR traffic and context-aware reliability and security provisioning.

This paper addresses the challenge of achieving high average User-Perceived Throughput (UPT) in multi-user downlink transmissions, which is essential for immersive experience in emerging 6G applications like extended reality and digital twins. Achieving high UPT involves effectively managing burst data traffic over fluctuating wireless channels, a topic not extensively explored in current literature. We approach this by modeling the system as a Markov Decision Process (MDP), focusing on maximizing average UPT by minimizing the average time users have non-empty queues while maintaining system stability. We introduce a multi-user scheduling scheme for a system with a single Resource Block (RB). Specifically, the scheduling metric is a fluid approximation of the minimum average time with non-empty queues, which is obtained by analytically solving a convex optimization problem. We next extend our scheduling scheme to handle multiple spectrum RBs through successive allocation of available RBs, achieving polynomial complexity. Using a quadratic Lyapunov drift method, we ensure scheduler stability. The scheduler is also modified to operate online, adapting to time-varying channel conditions. Simulation results demonstrate that our scheduler not only achieves near-optimal performance but also significantly outperforms existing benchmarks.

Integrated Sensing and Communications (ISAC) is an emerging 6G technology to address the increasing demands for ubiquitous sensing and communication. Achieving Ultra-Reliable and Low-Latency Communication (URLLC) with ISAC has been relatively under-investigated in the existing literature. In this paper, we investigate joint beamforming and scheduling for an ISAC-enabled system to fulfill URLLC requirements. We focus on a typical URLLC scenario where periodic and aperiodic traffic flows coexist. Specifically, the aperiodic traffic is triggered by sensing the stochastic environment. We propose a joint beamforming and scheduling scheme to sense the environment and transmit traffic simultaneously, where efficient scheduling is obtained by exploiting sensing results. To analyze the latency and reliability performance of the proposed scheme, we first describe the system as a Partially Observable Markov Decision Process (POMDP). Then, we analytically express the latency and reliability performance as the probability of successful transmission within the latency constraint. Based on this, we formulate an optimal performance tradeoff for the periodic and aperiodic traffic and obtain an optimal tradeoff by a Dynamic Programming (DP)-based algorithm. Furthermore, we reveal some insightful properties of the optimal tradeoff and the optimal policy. Simulation results show that the optimal tradeoff outperforms other tradeoffs of benchmark algorithms.

Extended reality (XR) is an emerging 6G application with unique traffic characteristics and requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. This paper investigates multi-user scheduling to meet XR services’ hard-latency constraints. Specifically, we focus on a periodical traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. We describe the system as a periodic Markov Decision Process (MDP) with the performance metric being the probability of successful transmission within the latency constraint. We then obtain the maximum success probability and the optimal scheduling based on the optimal value function. In the case of homogeneous arrivals, we construct a lower bound of the optimal value function. Based on this, we propose an online multi-user scheduling policy that determines scheduling decisions by solving a series of nonlinear Knapsack Problems (KPs) in polynomial time. Our analysis demonstrates that the scheduling scheme is asymptotically optimal with increasing users. Furthermore, we extend the online scheduling scheme to heterogeneous arrivals and present extensions for practical scenarios with multiple resource blocks, quasi-periodical arrivals, random frame sizes, and time-correlated channel fading. Finally, simulation results show that the proposed scheduler achieves near-optimal performance and outperforms other benchmark schedulers.

Achieving high reliability and low latency is a critical challenge for a wide range of applications that demand strict performance guarantees, such as real-time systems, industrial automation, and autonomous vehicles. Our primary focus is on ultra-reliable low-latency communication (URLLC), which aims to ensure a real-time requirement. We propose a solution for hard delay-constrained communication, which meets strict latency requirements by incorporating variable-length coding in short-packet transmission systems. Our approach utilizes a cross-layer design using truncated channel inversion transmission across parallel channels. This system can be characterized as a two-dimensional Markov chain, which consists of both the packet queue buffering bits to be encoded and symbol queue buffering coded symbols to be transmitted. By leveraging embedded Markov chains, we formulate an optimization problem to minimize the average power consumption while converting the problem into a one-dimensional Markov chain. We present a heuristic algorithm to obtain hard delay-constrained policies and utilize gradient descent policy to refine the policies and explore the trade-offs between hard delay constraints and power consumption.

In the forthcoming 6G era, Extended reality (XR) is an emerging application with unique traffic characteristics requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. In this paper, we investigate multi-user scheduling to meet hard-latency constraints for XR services. Specifically, we focus on a periodical XR traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. To find an optimal multi-user scheduling scheme, we first describe the system as a periodic Markov Decision Process (MDP), where the scheduling performance is expressed as the probability of successful transmission within the latency constraint. Then, we obtain the maximum success probability and the optimal scheduling based on the optimal value function. Inspired by the properties of the optimal value function, we construct a lower bound of it and propose an online multi-user scheduling scheme. In particular, scheduling decisions under the proposed scheme are determined by solving a series of nonlinear Knapsack Problem (KP) in polynomial time. Finally, simulation results show that the proposed scheduler achieves nearly optimal performance and outperforms other benchmark schedulers.

Finite blocklength coding has attracted considerable recent attention because it holds the promise of ultra-reliable and low-latency communications (URLLC) in smart grids, autonomous driving, tele-surgery, and industrial internet of things (IIoT). However, as the instantaneous blocklength is constrained by the number of backlogged bits, short packet transmission with random packet arrival becomes a challenging issue. In this paper, we present a cross-layer mechanism referred to as the buffer-aware variable-blocklength coding to minimize the average delay of bursty traffics. To optimize and analyze the buffer-aware short packet transmission, we formulate a tandem queue model consisting of both the packet queue buffering bits to be encoded and the symbol queue buffering coded symbols to be sent. By deriving the transition probability matrix and the steady state probability of the two-dimensional Markov chain characterizing the tandem queue, we obtain the average latency as a function of the arrival rate and transmission power. Simulation results verify our theoretical analysis and demonstrate the potential of the buffer-aware variable-blocklength coding scheme.

Integrated Sensing and Communications (ISAC) has been regarded as a technological trend in 6G networks to obtain significant performance gain with deep integration of sensing and communication functionalities. However, achieving Ultra-Reliable and Low-Latency Communication (URLLC) with ISAC designs has been under-investigated. In this paper, we investigate a joint beamforming and scheduling design for an ISAC-enabled system fulfilling URLLC requirements. In particular, we employ an ISAC design to sense stochastic events, through which an aperiodic traffic is generated. The event-triggered aperiodic traffic is next scheduled with a periodic traffic. Meanwhile, the system provides reliability for both periodic and aperiodic traffics within a hard latency constraint. To understand the latency and reliability performance under the joint design, we first describe the sensing and communication processes under a Partially Observable Markov Decision Process (POMDP) framework. Then, we analytically express the two traffics' latency and reliability performance under the joint beamforming and scheduling. Based on the analysis, we formulate an optimal tradeoff between the performance of the periodic and aperiodic traffics, and obtain it by a Dynamic Programming (DP)-based algorithm. Simulation results finally show that the optimal tradeoff outperforms other tradeoffs of benchmark algorithms.

To provide reliable transmissions with low-latency requirements, we focus on Finite-Blocklength Coding (FBC) in Ultra-Reliable and Low-Latency Communications (URLLC). However, ensuring the reliability and latency of FBC has remained an open issue in URLLC. In this paper, we develop a queue-aware FBC scheme under random arrivals. With the awareness of queue length, we employ variable-length coding to jointly encode packets, through which we obtain a benefit on reliability. Meanwhile, we optimize latency under a cross-layer approach, in which two classes of variable-length codes are investigated with resources allocated in the frequency and time domains, respectively. To obtain an optimal reliability-latency tradeoff under variable-length FBC, we first present the reliability and latency performance for single links based on a Constrained Markov Decision Process (CMDP). Providing reliability with a power allocation, we then obtain the optimal tradeoff by a Linear Programming (LP) problem, in which the probability of violation of the constraints on queue length and the number of transmitted packets is minimized under average constraints on resources. Moreover, we show an optimal threshold-based policy under Bernoulli arrivals. We finally consider some extensions of the optimal tradeoff for multi-user downlinks as well as single links with retransmission.

With over-the-air latency on the order of 0.1ms an-ticipated in 6G systems, the practical design of Finite-Blocklength Coding (FBC) has the potential to achieve extremely low latency communications. For this purpose, we focus on a joint FBC scheme in multi-user downlink systems. With a requirement of extremely low latency, we jointly encode data bits of multiple users over their orthogonal channel resources. As a result, we obtain throughput gain of the downlink transmission by an enlarged blocklength of FBC. In particular, we first present the joint encoding design for multiple downlink users by a matrix-based method. Under the multi-user joint FBC scheme, we then formulate an Integer Programming (IP) problem to maximize the throughput of downlink users subject to an average constraint on transmission power. By converting the derived IP problem to a nonlinear bipartite matching problem, we finally present a unified algorithm to obtain the optimal power-constrained throughput within the low latency requirement.

To enable a number of emerging applications, efforts from industry and academia have started to focus on defining 6G systems, in which more stringent requirements than those imposed on 5G systems are being considered. In particular, some 6G applications may require extremely low-latency on the order of 0.1ms, through which practical designs of channel coding can be investigated based on Finite-Blocklength Coding (FBC). In this paper, we focus on a joint FBC scheme over multi-user downlinks in the Industrial Internet of Things (IIoT), in which only several symbol durations are available for users within a requirement of extremely low-latency. Since a higher coding rate is obtained by enlarging the blocklength of FBC, we jointly encode users’ data bits over their allocated resources, through which an enlarged blocklength is attained. Specifically, we first formulate the multi-user joint encoding design with a matrix-based method. Then, we present the optimal power-constrained throughput within the extremely low-latency requirement by formulating a nonlinear bipartite matching problem. We finally demonstrate the benefit resulting from the joint FBC in terms of each user’s maximum obtainable distance. With the distance to each user varying, we also perform an analysis of the variation of the optimal power-constrained throughput.

Due to the stringent requirement of low-latency, Finite-Blocklength Coding (FBC) has been developed to guarantee reliability in Ultra-Reliable and Low-Latency Communications (URLLC). However, ensuring the reliability and latency of FBC for downlink transmissions has remained as an open issue for URLLC with random arrivals. In this paper, we focus on a multi-user downlink system with URLLC required. In the downlink transmission, we obtain a benefit on the reliability from a longer blocklength, generated by grouping and jointly encoding the multi-users' packets. By this means, a Joint Framing and Finite-Blocklength Coding (JF2BC) scheme is proposed to provide the requirements of reliability and latency. In particular, considering the queueing effects for latency under random arrivals, we employ a cross-layer approach to characterize the JF2BC policy. Under the queue-aware policy, we show an optimal tradeoff between the queueing delay and reliability by a Linear Programming (LP) problem, in which a matrix-based algorithm is developed to automatically generate the LP problem for the multi-user scenario. To optimize the tail distribution of queueing delay, we further extend the optimal tradeoff by using the violation probability of maximal queue length as the delay measure.

In this paper, delay-optimal and energy-efficient communication is studied for a single link under Markov random arrivals. We present the optimal tradeoff between delay and power over Additive White Gaussian Noise (AWGN) channels and extend the optimal tradeoff for block fading channels. Under time-correlated traffic arrivals, we develop a cross-layer solution that jointly considers the arrival rate, the queue length, and the channel state in order to minimize the average delay subject to a power constraint. For this purpose, we formulate the average delay and power problem as a Constrained Markov Decision Process (CMDP). Based on steady-state analysis for the CMDP, a Linear Programming (LP) problem is formulated to obtain the optimal delay-power tradeoff. We further show the optimal transmission strategy using a Lagrangian relaxation technique. Specifically, the optimal adaptive transmission is shown to have a threshold type of structure, where the thresholds on the queue length are presented for different transmission rates under the given arrival rates and channel states. By exploiting the result, we develop a threshold-based algorithm to efficiently obtain the optimal delay-power tradeoff. We show how a trajectory-sampling version of the proposed algorithm can be developed without the prior need of arrival statistics.

Ultra-Reliable and Low-Latency Communication (URLLC) has attracted significant attention due to its potential in factory automation, telesurgery, and automatic driving. However, little attention has been paid to URLLC when the traffic arrival is random. In this paper, a random arrival oriented URLLC policy is investigated for Additive White Gaussian Noise (AWGN) channels. More specifically, we develop a queue-aware variable-length channel coding scheme, in which the blocklength of channel coding is determined by the queue length. Further, from a cross-layer design perspective, we present the optimal tradeoff between latency and power consumption given the reliability constraint. To this end, we formulate a Markov chain to attain the delay and power consumption, based on which a Linear Programming (LP) problem is formulated to minimize the latency under a power constraint. By solving the derived LP problem, we obtain the optimal variable-length coding policies with a threshold-based structure imposed on the queue length.

Non-orthogonal multiple access (NOMA) has attracted great attention in the fifth-generation (5G) system to meet the rapidly increasing demand on quality of service (QoS). Among various QoS requirements, the urgent latency requirement has been expected to be provided for the delay-sensitive applications. In this paper, the delay-optimal uplink transmission in NOMA is studied over a block fading channel based on a cross-layer design, by which average latency is minimized with reliability provided by power allocation. In particular, the superposition coding in the physical layer and the scheduling in the network layer are jointly determined by the joint probabilities on the decisions of coding orders and transmission rates with the aware channel and queue states. With a constrained Markov decision process (CMDP), the cross-layer optimization is formulated to minimize the average delay subject to the constraints on power and reliability. The optimal delay-power tradeoff is obtained by formulating an equivalent linear programming (LP), which can be presented for multiple users based on a unified algorithm. Moreover, the optimal joint scheduling and superposition coding (JSSC) policy is constructed by using the structural properties. Based on the optimal cross-layer design, the transmission latency is optimized in the practical NOMA system.

As an emergency requirement of Quality of Service (QoS), low latency can be efficiently provided by a cross-layer scheduling. In particular, we consider a pair of Non-Orthogonal Multiple Access (NOMA) users with a random packet arrival over a block fading channel. A probabilistic cross-layer approach is considered to jointly determine the scheduling and superposition coding process, based on the buffer and channel state information. The joint scheduling and coding issue can be formulated as a two-dimension Markov chain, the state of which is a pair of queue lengths, based on which the average delay and power consumption can be obtained. A joint queue and channel aware optimization is formulated to minimize the average delay of packets given an average transmission power constraint. By converting the optimal problem into a linear programming, the optimal delay-power tradeoff can be obtained. We also discover that the optimal policy can be decomposed into a stationary superposition coding and a threshold-based scheduling policy.